We appreciate the time and effort spent by all the reviewers, and we are grateful for the useful comments and provided suggestions. We have carefully reviewed the critiques and we are happy to see that the panel was receptive to our proposal. The reviewers expressed three major concerns in the summary statement: (1) although the investigating team is well qualified our history of collaboration is short;(2) details regarding the practical constraints of the BARDOT system are lacking;(3) the machine learning techniques employed in the project are considered fairly standard. Below we briefly discuss the reviewers comments and indicate how we have changed our revised application to address the critique. (1) Dr. Dundar moved from industry to academia in the fall of 2008, at which point Dr. Rajwa (one of the original inventors of BARDOT) and Dr. Dundar began their collaboration on new approaches to the problem of non-exhaustively defined classes in phenotypic screening. This scientific partnership immediately produced interesting results, and at the time of submission of the original application, Dr. Dundar and Dr. Rajwa had their first manuscript under review. The approach presented in the original proposal was tested and the results were submitted to the ACM 15th Annual SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09), which is the largest and one of the most respected conferences in this field. The manuscript was accepted after a full peer review as one of the 50 regular papers selected from 551 submissions [20]. Following the proposal submission, research efforts continued and produced yet another approach to the problem described in this grant application. The preliminary findings are reported in a new manuscript which is currently under review [4]. (2) We rewrote the background and research methods sections of our proposal to include information re- quested by the reviewers regarding practical aspects of the BARDOT system, such as accuracy issues (Section D.3.2), frequency of encountering new, unknown classes (Section B.3.1), and validation (Section D.3.1). (3) The problem of phenotypic screening and classification of bacteria can be defined within exhaustive (stan- dard) or non-exhaustive learning frameworks. Although we agree that the implementation of an exhaustive clas- sification approach for BARDOT does require only fairly standard tools, the problem of the non-exhaustive nature of training libraries cannot be addressed by straightforward use of any textbook-level technique. In fact, the presence of non-exhaustively defined set of classes violates basic assumptions for most supervised learning systems. The issue of non-exhaustively defined classes is the major obstacle for application of machine learning in phenotypic analysis since the number of possible phenotypes may be infinite. In our original proposal we argued that learning with a non-exhaustively defined set of classes remains a very challenging problem, and presented evidence demonstrating that simple extensions of standard techniques cannot provide an acceptable solution. Subsequently, we proposed a new approach based on Bayesian simulation of classes and showed that preliminary results outperformed benchmark techniques [4]. Although these initial results looked promising, we did not consider the described preliminary algorithms final and definitive, and we do not believe that at this point we are able to provide an exact algorithmic solution to this complex problem. If we were able to do that, it would mean that we had already accomplished all the grant goals. The very essence of the proposed research is finding the answer to the defined problem, and the answer will remain unknown until after the work has been done. However, positive reviews and an acceptance of our work by KDD'09 conference judges, tell us that we are heading in the right direction. In the amended version of this application we propose a modified Bayesian approach based on Wishart priors (Section D.2.3). The algorithm creates new classes on the fly and evaluates maximum likelihood with the updated set of classes, gradually improving detection accuracy for future samples. We believe that this offers a substantial improvement over the previous method. Consequently, the preliminary results in Section C are updated to reflect our progress. Since the modified technique allows for classification with non-exhaustive and exhaustive sets using the same algorithm, we consolidated the previous specific aims 3 and 5 into one in the revised application.